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Liu JC, Ruan XH, Chun TT, Yao C, Huang D, Wong HL, Lai CT, Tsang CF, Ho SH, Ng TL, Xu DF, Na R. MRI T2w Radiomics-Based Machine Learning Models in Imaging Simulated Biopsy Add Diagnostic Value to PI-RADS in Predicting Prostate Cancer: A Retrospective Diagnostic Study. Cancers (Basel) 2024; 16:2944. [PMID: 39272801 PMCID: PMC11394278 DOI: 10.3390/cancers16172944] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/26/2024] [Revised: 08/16/2024] [Accepted: 08/19/2024] [Indexed: 09/15/2024] Open
Abstract
BACKGROUND Currently, prostate cancer (PCa) prebiopsy medical image diagnosis mainly relies on mpMRI and PI-RADS scores. However, PI-RADS has its limitations, such as inter- and intra-radiologist variability and the potential for imperceptible features. The primary objective of this study is to evaluate the effectiveness of a machine learning model based on radiomics analysis of MRI T2-weighted (T2w) images for predicting PCa in prebiopsy cases. METHOD A retrospective analysis was conducted using 820 lesions (363 cases, 457 controls) from The Cancer Imaging Archive (TCIA) Database for model development and validation. An additional 83 lesions (30 cases, 53 controls) from Hong Kong Queen Mary Hospital were used for independent external validation. The MRI T2w images were preprocessed, and radiomic features were extracted. Feature selection was performed using Cross Validation Least Angle Regression (CV-LARS). Using three different machine learning algorithms, a total of 18 prediction models and 3 shape control models were developed. The performance of the models, including the area under the curve (AUC) and diagnostic values such as sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV), were compared to the PI-RADS scoring system for both internal and external validation. RESULTS All the models showed significant differences compared to the shape control model (all p < 0.001, except SVM model PI-RADS+2 Features p = 0.004, SVM model PI-RADS+3 Features p = 0.002). In internal validation, the best model, based on the LR algorithm, incorporated 3 radiomic features (AUC = 0.838, sensitivity = 76.85%, specificity = 77.36%). In external validation, the LR (3 features) model outperformed PI-RADS in predictive value with AUC 0.870 vs. 0.658, sensitivity 56.67% vs. 46.67%, specificity 92.45% vs. 84.91%, PPV 80.95% vs. 63.64%, and NPV 79.03% vs. 73.77%. CONCLUSIONS The machine learning model based on radiomics analysis of MRI T2w images, along with simulated biopsy, provides additional diagnostic value to the PI-RADS scoring system in predicting PCa.
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Affiliation(s)
- Jia-Cheng Liu
- Department of Urology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China
| | - Xiao-Hao Ruan
- Department of Urology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China
| | - Tsun-Tsun Chun
- Department of Surgery, School of Clinical Medicine, LKS Faculty of Medicine, The University of Hong Kong, Hong Kong, China
| | - Chi Yao
- Department of Surgery, School of Clinical Medicine, LKS Faculty of Medicine, The University of Hong Kong, Hong Kong, China
| | - Da Huang
- Department of Urology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China
| | - Hoi-Lung Wong
- Department of Surgery, Queen Mary Hospital, Hong Kong, China
| | - Chun-Ting Lai
- Department of Surgery, Queen Mary Hospital, Hong Kong, China
| | - Chiu-Fung Tsang
- Department of Surgery, Queen Mary Hospital, Hong Kong, China
| | - Sze-Ho Ho
- Department of Surgery, Queen Mary Hospital, Hong Kong, China
| | - Tsui-Lin Ng
- Department of Surgery, Queen Mary Hospital, Hong Kong, China
| | - Dan-Feng Xu
- Department of Urology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China
| | - Rong Na
- Department of Surgery, School of Clinical Medicine, LKS Faculty of Medicine, The University of Hong Kong, Hong Kong, China
- Department of Surgery, Queen Mary Hospital, Hong Kong, China
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Gülbahar Ateş S, Demirel BB, Kekilli E, Öztürk E, Uçmak G. Primary tumor heterogeneity on pre-treatment [68Ga]Ga-PSMA PET/CT for the prediction of biochemical recurrence in prostate cancer. Rev Esp Med Nucl Imagen Mol 2024:500032. [PMID: 39097169 DOI: 10.1016/j.remnie.2024.500032] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/12/2024] [Revised: 06/11/2024] [Accepted: 06/12/2024] [Indexed: 08/05/2024]
Abstract
PURPOSE The aim of this study is to research the value of the texture analysis of primary tumors in pre-treatment [68Ga]Ga-PSMA PET in the prediction of the development of biochemical recurrence (BCR) in prostate cancer patients who underwent definitive therapies. METHODS 51 patients with prostate adenocarcinoma who had a pre-treatment [68Ga]Ga-PSMA-11 PET/CT and underwent definitive radiotherapy (RT) or radical prostatectomy (RP) were included in the study. Demographics, clinicopathologic features, the presence of BCR, and the last follow-up date of patients were recorded. Textural and conventional PET parameters (maximum standardized uptake value (SUVmax), total lesion-PSMA (TL-PSMA), and PSMA-tumor volume (PSMA-TV)) were obtained from PET/CT images using LifeX program. Parameters were grouped using the Youden index in ROC analysis. Factors predicting the BCR were determined using Cox regression analyses. RESULTS 29 (56.9%) patients have received primary curative RT, while the remaining 22 (43.1%) patients have undergone RP. 5 (22.7%) patients with RP and 3 (10.3%) patients with curative RT have developed BCR during the follow-up. INTENSITY-BASED-minimum grey level (P=.050), GLCM-sum variance (P=.019), and GLCM-cluster prominence (P=.050) were associated with BCR in univariate analysis. INTENSITY-BASED-minimum grey level (P=.009) and GLCM-sum variance (P=.004) were found as independent predictors of BCR in the multivariate analysis. CONCLUSION Tumor heterogeneity on pre-treatment [68Ga]Ga-PSMA PET is associated with a high risk of BCR in PCa patients who underwent definitive therapies.
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Affiliation(s)
- Seda Gülbahar Ateş
- Department of Nuclear Medicine, Hitit University Erol Olçok Training and Research Hospital, Çorum, Turkey.
| | - Bedriye Büşra Demirel
- Department of Nuclear Medicine, Dr. Abdurrahman Yurtaslan Ankara Oncology Training and Research Hospital, University of Health Sciences, Ankara, Turkey
| | - Esra Kekilli
- Department of Radiation Oncology, Dr. Abdurrahman Yurtaslan Ankara Oncology Training and Research Hospital, University of Health Sciences, Ankara, Turkey
| | - Erdem Öztürk
- Department of Urology, Dr. Abdurrahman Yurtaslan Ankara Oncology Training and Research Hospital, University of Health Sciences, Ankara, Turkey
| | - Gülin Uçmak
- Department of Nuclear Medicine, Dr. Abdurrahman Yurtaslan Ankara Oncology Training and Research Hospital, University of Health Sciences, Ankara, Turkey
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Urso L, Cittanti C, Manco L, Ortolan N, Borgia F, Malorgio A, Scribano G, Mastella E, Guidoboni M, Stefanelli A, Turra A, Bartolomei M. ML Models Built Using Clinical Parameters and Radiomic Features Extracted from 18F-Choline PET/CT for the Prediction of Biochemical Recurrence after Metastasis-Directed Therapy in Patients with Oligometastatic Prostate Cancer. Diagnostics (Basel) 2024; 14:1264. [PMID: 38928679 PMCID: PMC11202947 DOI: 10.3390/diagnostics14121264] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/16/2024] [Revised: 06/09/2024] [Accepted: 06/12/2024] [Indexed: 06/28/2024] Open
Abstract
Oligometastatic patients at [18F]F-Fluorocholine (18F-choline) PET/CT may be treated with metastasis-directed therapy (MDT). The aim of this study was to combine radiomic parameters extracted from 18F-choline PET/CT and clinical data to build machine learning (ML) models able to predict MDT efficacy. METHODS Oligorecurrent patients (≤5 lesions) at 18F-choline PET/CT and treated with MDT were collected. A per-patient and per-lesion analysis was performed, using 2-year biochemical recurrence (BCR) after MDT as the standard of reference. Clinical parameters and radiomic features (RFts) extracted from 18F-choline PET/CT were used for training five ML Models for both CT and PET images. The performance metrics were calculated (i.e., Area Under the Curve-AUC; Classification Accuracy-CA). RESULTS A total of 46 metastases were selected and segmented in 29 patients. BCR after MDT occurred in 20 (69%) patients after 2 years of follow-up. In total, 73 and 33 robust RFTs were selected from CT and PET datasets, respectively. PET ML Models showed better performances than CT Models for discriminating BCR after MDT, with Stochastic Gradient Descent (SGD) being the best model (AUC = 0.95; CA = 0.90). CONCLUSION ML Models built using clinical parameters and CT and PET RFts extracted via 18F-choline PET/CT can accurately predict BCR after MDT in oligorecurrent PCa patients. If validated externally, ML Models could improve the selection of oligorecurrent PCa patients for treatment with MDT.
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Affiliation(s)
- Luca Urso
- Department of Translational Medicine, University of Ferrara, 44121 Ferrara, Italy; (L.U.); (C.C.); (N.O.); (F.B.); (M.G.)
- Nuclear Medicine Unit, Onco-Hematology Department, University Hospital of Ferrara, 44124 Ferrara, Italy;
| | - Corrado Cittanti
- Department of Translational Medicine, University of Ferrara, 44121 Ferrara, Italy; (L.U.); (C.C.); (N.O.); (F.B.); (M.G.)
- Nuclear Medicine Unit, Onco-Hematology Department, University Hospital of Ferrara, 44124 Ferrara, Italy;
| | - Luigi Manco
- Medical Physics Unit, University Hospital of Ferrara, 44124 Ferrara, Italy; (E.M.); (A.T.)
| | - Naima Ortolan
- Department of Translational Medicine, University of Ferrara, 44121 Ferrara, Italy; (L.U.); (C.C.); (N.O.); (F.B.); (M.G.)
- Nuclear Medicine Unit, Onco-Hematology Department, University Hospital of Ferrara, 44124 Ferrara, Italy;
| | - Francesca Borgia
- Department of Translational Medicine, University of Ferrara, 44121 Ferrara, Italy; (L.U.); (C.C.); (N.O.); (F.B.); (M.G.)
- Nuclear Medicine Unit, Onco-Hematology Department, University Hospital of Ferrara, 44124 Ferrara, Italy;
| | - Antonio Malorgio
- U.O.C. Radiotherapy, University Hospital of Ferrara, 44124 Ferrara, Italy; (A.M.); (A.S.)
| | - Giovanni Scribano
- Department of Physics and Earth Science, University of Ferrara, 44121 Ferrara, Italy;
| | - Edoardo Mastella
- Medical Physics Unit, University Hospital of Ferrara, 44124 Ferrara, Italy; (E.M.); (A.T.)
| | - Massimo Guidoboni
- Department of Translational Medicine, University of Ferrara, 44121 Ferrara, Italy; (L.U.); (C.C.); (N.O.); (F.B.); (M.G.)
- U.O.C. Clinical Oncology, University Hospital of Ferrara, 44124 Ferrara, Italy
| | - Antonio Stefanelli
- U.O.C. Radiotherapy, University Hospital of Ferrara, 44124 Ferrara, Italy; (A.M.); (A.S.)
| | - Alessandro Turra
- Medical Physics Unit, University Hospital of Ferrara, 44124 Ferrara, Italy; (E.M.); (A.T.)
| | - Mirco Bartolomei
- Nuclear Medicine Unit, Onco-Hematology Department, University Hospital of Ferrara, 44124 Ferrara, Italy;
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Alanezi ST, Kraśny MJ, Kleefeld C, Colgan N. Differential Diagnosis of Prostate Cancer Grade to Augment Clinical Diagnosis Based on Classifier Models with Tuned Hyperparameters. Cancers (Basel) 2024; 16:2163. [PMID: 38893281 PMCID: PMC11171700 DOI: 10.3390/cancers16112163] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/20/2024] [Revised: 05/25/2024] [Accepted: 05/30/2024] [Indexed: 06/21/2024] Open
Abstract
We developed a novel machine-learning algorithm to augment the clinical diagnosis of prostate cancer utilizing first and second-order texture analysis metrics in a novel application of machine-learning radiomics analysis. We successfully discriminated between significant prostate cancers versus non-tumor regions and provided accurate prediction between Gleason score cohorts with statistical sensitivity of 0.82, 0.81 and 0.91 in three separate pathology classifications. Tumor heterogeneity and prediction of the Gleason score were quantified using two feature selection approaches and two separate classifiers with tuned hyperparameters. There was a total of 71 patients analyzed in this study. Multiparametric MRI, incorporating T2WI and ADC maps, were used to derive radiomics features. Recursive feature elimination (RFE), the least absolute shrinkage and selection operator (LASSO), and two classification approaches, incorporating a support vector machine (SVM) (with randomized search) and random forest (RF) (with grid search), were utilized to differentiate between non-tumor regions and significant cancer while also predicting the Gleason score. In T2WI images, the RFE feature selection approach combined with RF and SVM classifiers outperformed LASSO with SVM and RF classifiers. The best performance was achieved by combining LASSO and SVM into a model that used both T2WI and ADC images. This model had an area under the curve (AUC) of 0.91. Radiomic features computed from ADC and T2WI images were used to predict three groups of Gleason score using two kinds of feature selection methods (RFE and LASSO), RF and SVM classifier models with tuned hyperparameters. Using combined sequences (T2WI and ADC map images) and combined radiomics (1st and GLCM features), LASSO, with a feature selection method with RF, was able to predict G3 with the highest sensitivity at a level AUC of 0.92. To predict G3 for single sequence (T2WI images) using GLCM features, LASSO with SVM achieved the highest sensitivity with an AUC of 0.92.
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Affiliation(s)
- Saleh T. Alanezi
- Department of Physics, College of Science, Northern Border University, Arar P.O. Box 1321, Saudi Arabia
- Department of Physics, School of Natural Sciences, College of Science and Engineering, University of Galway, H91 TK33 Galway, Ireland; (M.J.K.); (C.K.); (N.C.)
| | - Marcin Jan Kraśny
- Department of Physics, School of Natural Sciences, College of Science and Engineering, University of Galway, H91 TK33 Galway, Ireland; (M.J.K.); (C.K.); (N.C.)
- Translational Medical Device Lab (TMDLab), Lambe Institute for Translational Research, University of Galway, H91 V4AY Galway, Ireland
| | - Christoph Kleefeld
- Department of Physics, School of Natural Sciences, College of Science and Engineering, University of Galway, H91 TK33 Galway, Ireland; (M.J.K.); (C.K.); (N.C.)
| | - Niall Colgan
- Department of Physics, School of Natural Sciences, College of Science and Engineering, University of Galway, H91 TK33 Galway, Ireland; (M.J.K.); (C.K.); (N.C.)
- Faculty of Engineering & Informatics, Technological University of the Shannon, N37 HD68 Athlone, Ireland
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Hu C, Qiao X, Hu C, Cao C, Wang X, Bao J. The practical clinical role of machine learning models with different algorithms in predicting prostate cancer local recurrence after radical prostatectomy. Cancer Imaging 2024; 24:23. [PMID: 38326860 PMCID: PMC10848341 DOI: 10.1186/s40644-024-00667-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/26/2023] [Accepted: 01/21/2024] [Indexed: 02/09/2024] Open
Abstract
BACKGROUND The detection of local recurrence for prostate cancer (PCa) patients following radical prostatectomy (RP) is challenging and can influence the treatment plan. Our aim was to construct and verify machine learning models with three different algorithms based on post-operative mpMRI for predicting local recurrence of PCa after RP and explore their potential clinical value compared with the Prostate Imaging for Recurrence Reporting (PI-RR) score of expert-level radiologists. METHODS A total of 176 patients were retrospectively enrolled and randomly divided into training (n = 123) and testing (n = 53) sets. The PI-RR assessments were performed by two expert-level radiologists with access to the operative histopathological and pre-surgical clinical results. The radiomics models to predict local recurrence were built by utilizing three different algorithms (i.e., support vector machine [SVM], linear discriminant analysis [LDA], and logistic regression-least absolute shrinkage and selection operator [LR-LASSO]). The combined model integrating radiomics features and PI-RR score was developed using the most effective classifier. The classification performances of the proposed models were assessed by receiver operating characteristic (ROC) curve analysis. RESULTS There were no significant differences between the training and testing sets concerning age, prostate-specific antigen (PSA), Gleason score, T-stage, seminal vesicle invasion (SVI), perineural invasion (PNI), and positive surgical margins (PSM). The radiomics model based on LR-LASSO exhibited superior performance than other radiomics models, with an AUC of 0.858 in the testing set; the PI-RR yielded an AUC of 0.833, and there was no significant difference between the best radiomics model and the PI-RR score. The combined model achieved the best predictive performance with an AUC of 0.924, and a significant difference was observed between the combined model and PI-RR score. CONCLUSIONS Our radiomics model is an effective tool to predict PCa local recurrence after RP. By integrating radiomics features with the PI-RR score, our combined model exhibited significantly better predictive performance of local recurrence than expert-level radiologists' PI-RR assessment.
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Affiliation(s)
- Chenhan Hu
- Department of Radiology, the First Affiliated Hospital of Soochow University, 188#, Shizi Road, Suzhou, 215006, China
| | - Xiaomeng Qiao
- Department of Radiology, the First Affiliated Hospital of Soochow University, 188#, Shizi Road, Suzhou, 215006, China
| | - Chunhong Hu
- Department of Radiology, the First Affiliated Hospital of Soochow University, 188#, Shizi Road, Suzhou, 215006, China
| | - Changhao Cao
- Department of Radiology, the First Affiliated Hospital of Soochow University, 188#, Shizi Road, Suzhou, 215006, China
| | - Ximing Wang
- Department of Radiology, the First Affiliated Hospital of Soochow University, 188#, Shizi Road, Suzhou, 215006, China.
| | - Jie Bao
- Department of Radiology, the First Affiliated Hospital of Soochow University, 188#, Shizi Road, Suzhou, 215006, China.
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Pan J, Zhang T, Chen S, Bu T, Zhao J, Ni X, Shi B, Gan H, Wei Y, Wang Q, Wang B, Wu J, Song S, Wang F, Liu C, Ye D, Zhu Y. Nomogram to predict the presence of PSMA-negative but FDG-positive lesion in castration-resistant prostate cancer: a multicenter cohort study. Ther Adv Med Oncol 2024; 16:17588359231220506. [PMID: 38188464 PMCID: PMC10771757 DOI: 10.1177/17588359231220506] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/16/2023] [Accepted: 11/21/2023] [Indexed: 01/09/2024] Open
Abstract
Background PSMA-negative but FDG-positive (PSMA-/FDG+) lesion in dual-tracer (68Ga-PSMA and 18F-FDG) positron emission tomography/computed tomography (PET/CT) is associated with an unfavorable response to Lutetium-177 (177Lu)-PSMA-617. This study sought to develop both radiomics and clinical models for the precise prediction of the presence of PSMA-/FDG+ lesions in patients with castration-resistant prostate cancer (CPRC). Methods A cohort of 298 patients who underwent dual-tracer PET/CT with a less than 5-day interval was included. The evaluation of the prognostic performance of the radiomics model drew upon the survival data derived from 40 patients with CRPC treated with 177Lu-PSMA-617 in an external cohort. Two endpoints were evaluated: (a) prostate-specific antigen (PSA) response rate, defined as a reduction exceeding 50% from baseline and (b) overall survival (OS), measured from the initiation of 177Lu-PSMA-617 to death from any cause. Results PSMA-/FDG+ lesions were identified in 56 (18.8%) CRPC patients. Both radiomics (area under the curve [AUC], 0.83) and clinical models (AUC, 0.78) demonstrated robust performance in PSMA-/FDG+ lesion prediction. Decision curve analysis revealed that the radiomics model yielded a net benefit over the 'screen all' strategy at a threshold probability of ⩾4%. At a 5% probability threshold, the radiomics model facilitated a 21% reduction in 18F-FDG PET/CT scans while only missing 2% of PSMA-/FDG+ cases. Patients with a low estimated score exhibited significantly prolonged OS (hazard ratio = 0.49, p = 0.029) and a higher PSA response rate (75% versus 35%, p = 0.011) compared to those with a high estimated score. Conclusion This study successfully developed two models with accurate estimations of the risk associated with PSMA-/FDG+ lesions in CRPC patients. These models held potential utility in aiding the selection of candidates for 177Lu-PSMA-617 treatment and guiding 68Ga-PSMA PET/CT-directed radiotherapy.
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Affiliation(s)
- Jian Pan
- Department of Urology, Fudan University Shanghai Cancer Center, Shanghai, China
- Shanghai Genitourinary Cancer Institute, Shanghai, China
- Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China
| | - Tingwei Zhang
- Department of Urology, Fudan University Shanghai Cancer Center, Shanghai, China
- Shanghai Genitourinary Cancer Institute, Shanghai, China
- Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China
| | - Shouzhen Chen
- Department of Urology, Qilu Hospital, Cheeloo College of Medicine, Shandong University, Shandong Province, China
| | - Ting Bu
- Department of Nuclear Medicine, Nanjing First Hospital, Nanjing Medical University, Nanjing, China
- Department of Nuclear Medicine, The Affiliated Jiangning Hospital with Nanjing Medical University, Nanjing, China
| | - Jinou Zhao
- Department of Urology, Fudan University Shanghai Cancer Center, Shanghai, China
- Shanghai Genitourinary Cancer Institute, Shanghai, China
- Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China
| | - Xudong Ni
- Department of Urology, Fudan University Shanghai Cancer Center, Shanghai, China
- Shanghai Genitourinary Cancer Institute, Shanghai, China
- Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China
| | - Benkang Shi
- Department of Urology, Qilu Hospital, Cheeloo College of Medicine, Shandong University, Shandong Province, China
| | - Hualei Gan
- Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China
- Department of Pathology, Fudan University Shanghai Cancer Center, Shanghai, China
| | - Yu Wei
- Department of Urology, Fudan University Shanghai Cancer Center, Shanghai, China
- Shanghai Genitourinary Cancer Institute, Shanghai, China
- Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China
| | - Qifeng Wang
- Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China
- Department of Pathology, Fudan University Shanghai Cancer Center, Shanghai, China
| | - Beihe Wang
- Department of Urology, Fudan University Shanghai Cancer Center, Shanghai, China
- Shanghai Genitourinary Cancer Institute, Shanghai, China
- Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China
| | - Junlong Wu
- Department of Urology, Fudan University Shanghai Cancer Center, Shanghai, China
- Shanghai Genitourinary Cancer Institute, Shanghai, China
- Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China
| | - Shaoli Song
- Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China
- Department of Nuclear Medicine, Fudan University Shanghai Cancer Center, Shanghai, China
| | - Feng Wang
- Department of Nuclear Medicine, Nanjing First Hospital, Nanjing Medical University, Nanjing, China
| | - Chang Liu
- Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China
- Department of Nuclear Medicine, Fudan University Shanghai Cancer Center, Shanghai, China
| | - Dingwei Ye
- Department of Urology, Fudan University Shanghai Cancer Center, Shanghai, China
- Shanghai Genitourinary Cancer Institute, Shanghai, China
- Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China
| | - Yao Zhu
- Department of Urology, Fudan University Shanghai Cancer Center, Shanghai, China
- Shanghai Genitourinary Cancer Institute, Shanghai, China
- Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China
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Nicoletti G, Mazzetti S, Maimone G, Cignini V, Cuocolo R, Faletti R, Gatti M, Imbriaco M, Longo N, Ponsiglione A, Russo F, Serafini A, Stanzione A, Regge D, Giannini V. Development and Validation of an Explainable Radiomics Model to Predict High-Aggressive Prostate Cancer: A Multicenter Radiomics Study Based on Biparametric MRI. Cancers (Basel) 2024; 16:203. [PMID: 38201630 PMCID: PMC10778513 DOI: 10.3390/cancers16010203] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/15/2023] [Revised: 12/19/2023] [Accepted: 12/29/2023] [Indexed: 01/12/2024] Open
Abstract
In the last years, several studies demonstrated that low-aggressive (Grade Group (GG) ≤ 2) and high-aggressive (GG ≥ 3) prostate cancers (PCas) have different prognoses and mortality. Therefore, the aim of this study was to develop and externally validate a radiomic model to noninvasively classify low-aggressive and high-aggressive PCas based on biparametric magnetic resonance imaging (bpMRI). To this end, 283 patients were retrospectively enrolled from four centers. Features were extracted from apparent diffusion coefficient (ADC) maps and T2-weighted (T2w) sequences. A cross-validation (CV) strategy was adopted to assess the robustness of several classifiers using two out of the four centers. Then, the best classifier was externally validated using the other two centers. An explanation for the final radiomics signature was provided through Shapley additive explanation (SHAP) values and partial dependence plots (PDP). The best combination was a naïve Bayes classifier trained with ten features that reached promising results, i.e., an area under the receiver operating characteristic (ROC) curve (AUC) of 0.75 and 0.73 in the construction and external validation set, respectively. The findings of our work suggest that our radiomics model could help distinguish between low- and high-aggressive PCa. This noninvasive approach, if further validated and integrated into a clinical decision support system able to automatically detect PCa, could help clinicians managing men with suspicion of PCa.
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Affiliation(s)
- Giulia Nicoletti
- Department of Electronics and Telecommunications, Polytechnic of Turin, Corso Duca degli Abruzzi, 24, 10129 Turin, Italy;
- Department of Surgical Sciences, University of Turin, Corso Dogliotti, 14, 10126 Turin, Italy; (V.C.); (R.F.); (A.S.)
| | - Simone Mazzetti
- Radiology Unit, Candiolo Cancer Institute, FPO-IRCCS, Strada Provinciale, 142—KM 3.95, 10060 Candiolo, Italy; (S.M.); (G.M.); (F.R.); (D.R.)
| | - Giovanni Maimone
- Radiology Unit, Candiolo Cancer Institute, FPO-IRCCS, Strada Provinciale, 142—KM 3.95, 10060 Candiolo, Italy; (S.M.); (G.M.); (F.R.); (D.R.)
| | - Valentina Cignini
- Department of Surgical Sciences, University of Turin, Corso Dogliotti, 14, 10126 Turin, Italy; (V.C.); (R.F.); (A.S.)
| | - Renato Cuocolo
- Department of Medicine, Surgery, and Dentistry, University of Salerno, Via Salvador Allende, 43, 84081 Baronissi, Italy;
| | - Riccardo Faletti
- Department of Surgical Sciences, University of Turin, Corso Dogliotti, 14, 10126 Turin, Italy; (V.C.); (R.F.); (A.S.)
| | - Marco Gatti
- Department of Surgical Sciences, University of Turin, Corso Dogliotti, 14, 10126 Turin, Italy; (V.C.); (R.F.); (A.S.)
| | - Massimo Imbriaco
- Department of Advanced Biomedical Sciences, University of Naples “Federico II”, Via Pansini, 5, 80131 Naples, Italy; (M.I.); (A.P.)
| | - Nicola Longo
- Department of Neurosciences, Reproductive Sciences and Odontostomatology, University of Naples “Federico II”, Via Pansini, 5, 80131 Naples, Italy;
| | - Andrea Ponsiglione
- Department of Advanced Biomedical Sciences, University of Naples “Federico II”, Via Pansini, 5, 80131 Naples, Italy; (M.I.); (A.P.)
| | - Filippo Russo
- Radiology Unit, Candiolo Cancer Institute, FPO-IRCCS, Strada Provinciale, 142—KM 3.95, 10060 Candiolo, Italy; (S.M.); (G.M.); (F.R.); (D.R.)
| | - Alessandro Serafini
- Department of Surgical Sciences, University of Turin, Corso Dogliotti, 14, 10126 Turin, Italy; (V.C.); (R.F.); (A.S.)
| | - Arnaldo Stanzione
- Department of Advanced Biomedical Sciences, University of Naples “Federico II”, Via Pansini, 5, 80131 Naples, Italy; (M.I.); (A.P.)
| | - Daniele Regge
- Radiology Unit, Candiolo Cancer Institute, FPO-IRCCS, Strada Provinciale, 142—KM 3.95, 10060 Candiolo, Italy; (S.M.); (G.M.); (F.R.); (D.R.)
- Department of Translational Research, Via Risorgimento, 36, University of Pisa, 56126 Pisa, Italy
| | - Valentina Giannini
- Department of Surgical Sciences, University of Turin, Corso Dogliotti, 14, 10126 Turin, Italy; (V.C.); (R.F.); (A.S.)
- Radiology Unit, Candiolo Cancer Institute, FPO-IRCCS, Strada Provinciale, 142—KM 3.95, 10060 Candiolo, Italy; (S.M.); (G.M.); (F.R.); (D.R.)
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8
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Huang W, Tao Z, Younis MH, Cai W, Kang L. Nuclear medicine radiomics in digestive system tumors: Concept, applications, challenges, and future perspectives. VIEW 2023; 4:20230032. [PMID: 38179181 PMCID: PMC10766416 DOI: 10.1002/viw.20230032] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/22/2023] [Accepted: 07/20/2023] [Indexed: 01/06/2024] Open
Abstract
Radiomics aims to develop novel biomarkers and provide relevant deeper subvisual information about pathology, immunophenotype, and tumor microenvironment. It uses automated or semiautomated quantitative analysis of high-dimensional images to improve characterization, diagnosis, and prognosis. Recent years have seen a rapid increase in radiomics applications in nuclear medicine, leading to some promising research results in digestive system oncology, which have been driven by big data analysis and the development of artificial intelligence. Although radiomics advances one step further toward the non-invasive precision medical analysis, it is still a step away from clinical application and faces many challenges. This review article summarizes the available literature on digestive system tumors regarding radiomics in nuclear medicine. First, we describe the workflow and steps involved in radiomics analysis. Subsequently, we discuss the progress in clinical application regarding the utilization of radiomics for distinguishing between various diseases and evaluating their prognosis, and demonstrate how radiomics advances this field. Finally, we offer our viewpoint on how the field can progress by addressing the challenges facing clinical implementation.
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Affiliation(s)
- Wenpeng Huang
- Department of Nuclear Medicine, Peking University First Hospital, Beijing, China
| | - Zihao Tao
- Department of Nuclear Medicine, Peking University First Hospital, Beijing, China
| | - Muhsin H. Younis
- Departments of Radiology and Medical Physics, University of Wisconsin-Madison, Madison, Wisconsin, USA
| | - Weibo Cai
- Departments of Radiology and Medical Physics, University of Wisconsin-Madison, Madison, Wisconsin, USA
| | - Lei Kang
- Department of Nuclear Medicine, Peking University First Hospital, Beijing, China
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9
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Li Y, Li F, Han S, Ning J, Su P, Liu J, Qu L, Huang S, Wang S, Li X, Li X. Performance of 18F-DCFPyL PET/CT in Primary Prostate Cancer Diagnosis, Gleason Grading and D'Amico Classification: A Radiomics-Based Study. PHENOMICS (CHAM, SWITZERLAND) 2023; 3:576-585. [PMID: 38223686 PMCID: PMC10781655 DOI: 10.1007/s43657-023-00108-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/01/2022] [Revised: 04/06/2023] [Accepted: 04/13/2023] [Indexed: 01/16/2024]
Abstract
This study aimed to investigate the performance of 18F-DCFPyL positron emission tomography/computerized tomography (PET/CT) models for predicting benign-vs-malignancy, high pathological grade (Gleason score > 7), and clinical D'Amico classification with machine learning. The study included 138 patients with treatment-naïve prostate cancer presenting positive 18F-DCFPyL scans. The primary lesions were delineated on PET images, followed by the extraction of tumor-to-background-based general and higher-order textural features by applying five different binning approaches. Three layer-machine learning approaches were used to identify relevant in vivo features and patient characteristics and their relative weights for predicting high-risk malignant disease. The weighted features were integrated and implemented to establish individual predictive models for malignancy (Mm), high path-risk lesions (by Gleason score) (Mgs), and high clinical risk disease (by amico) (Mamico). The established models were validated in a Monte Carlo cross-validation scheme. In patients with all primary prostate cancer, the highest areas under the curve for our models were calculated. The performance of established models as revealed by the Monte Carlo cross-validation presenting as the area under the receiver operator characteristic curve (AUC): 0.97 for Mm, AUC: 0.73 for Mgs, AUC: 0.82 for Mamico. Our study demonstrated the clinical potential of 18F-DCFPyL PET/CT radiomics in distinguishing malignant from benign prostate tumors, and high-risk tumors, without biopsy sampling. And in vivo 18F-DCFPyL PET/CT can be considered a noninvasive tool for virtual biopsy for personalized treatment management. Supplementary Information The online version contains supplementary material available at 10.1007/s43657-023-00108-y.
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Affiliation(s)
- Yuekai Li
- Department of Nuclear Medicine, Qilu Hospital of Shandong University, No. 107, Cultural West Road, Jinan, 250012 China
| | - Fengcai Li
- Department of Hepatology, Qilu Hospital of Shandong University, Wenhuaxi Road 107#, Jinan, 250012 China
| | - Shaoli Han
- Evomics Medical Technology Co., Ltd, Shanghai, 201203 China
| | - Jing Ning
- Evomics Medical Technology Co., Ltd, Shanghai, 201203 China
| | - Peng Su
- Department of Nuclear Medicine, Qilu Hospital of Shandong University, No. 107, Cultural West Road, Jinan, 250012 China
| | - Jianfeng Liu
- Department of Nuclear Medicine, Qilu Hospital of Shandong University, No. 107, Cultural West Road, Jinan, 250012 China
| | - Lili Qu
- Department of Nuclear Medicine, Qilu Hospital of Shandong University, No. 107, Cultural West Road, Jinan, 250012 China
| | - Shuai Huang
- Department of Nuclear Medicine, Qilu Hospital of Shandong University, No. 107, Cultural West Road, Jinan, 250012 China
| | - Shiwei Wang
- Evomics Medical Technology Co., Ltd, Shanghai, 201203 China
| | - Xin Li
- Department of Nuclear Medicine, Qilu Hospital of Shandong University, No. 107, Cultural West Road, Jinan, 250012 China
| | - Xiang Li
- Division of Nuclear Medicine, Department of Biomedical Imaging and Image-Guided Therapy, Vienna General Hospital, Medical University of Vienna, 1090 Vienna, Austria
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10
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Luining WI, Oprea-Lager DE, Vis AN, van Moorselaar RJA, Knol RJJ, Wondergem M, Boellaard R, Cysouw MCF. Optimization and validation of 18F-DCFPyL PET radiomics-based machine learning models in intermediate- to high-risk primary prostate cancer. PLoS One 2023; 18:e0293672. [PMID: 37943772 PMCID: PMC10635444 DOI: 10.1371/journal.pone.0293672] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/25/2023] [Accepted: 10/17/2023] [Indexed: 11/12/2023] Open
Abstract
INTRODUCTION Radiomics extracted from prostate-specific membrane antigen (PSMA)-PET modeled with machine learning (ML) may be used for prediction of disease risk. However, validation of previously proposed approaches is lacking. We aimed to optimize and validate ML models based on 18F-DCFPyL-PET radiomics for the prediction of lymph-node involvement (LNI), extracapsular extension (ECE), and postoperative Gleason score (GS) in primary prostate cancer (PCa) patients. METHODS Patients with intermediate- to high-risk PCa who underwent 18F-DCFPyL-PET/CT before radical prostatectomy with pelvic lymph-node dissection were evaluated. The training dataset included 72 patients, the internal validation dataset 24 patients, and the external validation dataset 27 patients. PSMA-avid intra-prostatic lesions were delineated semi-automatically on PET and 480 radiomics features were extracted. Conventional PET-metrics were derived for comparative analysis. Segmentation, preprocessing, and ML methods were optimized in repeated 5-fold cross-validation (CV) on the training dataset. The trained models were tested on the combined validation dataset. Combat harmonization was applied to external radiomics data. Model performance was assessed using the receiver-operating-characteristics curve (AUC). RESULTS The CV-AUCs in the training dataset were 0.88, 0.79 and 0.84 for LNI, ECE, and GS, respectively. In the combined validation dataset, the ML models could significantly predict GS with an AUC of 0.78 (p<0.05). However, validation AUCs for LNI and ECE prediction were not significant (0.57 and 0.63, respectively). Conventional PET metrics-based models had comparable AUCs for LNI (0.59, p>0.05) and ECE (0.66, p>0.05), but a lower AUC for GS (0.73, p<0.05). In general, Combat harmonization improved external validation AUCs (-0.03 to +0.18). CONCLUSION In internal and external validation, 18F-DCFPyL-PET radiomics-based ML models predicted high postoperative GS but not LNI or ECE in intermediate- to high-risk PCa. Therefore, the clinical benefit seems to be limited. These results underline the need for external and/or multicenter validation of PET radiomics-based ML model analyses to assess their generalizability.
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Affiliation(s)
- Wietske I. Luining
- Department of Urology, Amsterdam University Medical Centers, Prostate Cancer Network Netherlands, Amsterdam, The Netherlands
- Department of Radiology & Nuclear Medicine, Amsterdam University Medical Centers, Cancer Center Amsterdam, Amsterdam, The Netherlands
| | - Daniela E. Oprea-Lager
- Department of Radiology & Nuclear Medicine, Amsterdam University Medical Centers, Cancer Center Amsterdam, Amsterdam, The Netherlands
| | - André N. Vis
- Department of Urology, Amsterdam University Medical Centers, Prostate Cancer Network Netherlands, Amsterdam, The Netherlands
| | - Reindert J. A. van Moorselaar
- Department of Urology, Amsterdam University Medical Centers, Prostate Cancer Network Netherlands, Amsterdam, The Netherlands
| | - Remco J. J. Knol
- Department of Nuclear Medicine, Northwest Clinics, Alkmaar, The Netherlands
| | - Maurits Wondergem
- Department of Nuclear Medicine, Northwest Clinics, Alkmaar, The Netherlands
| | - Ronald Boellaard
- Department of Radiology & Nuclear Medicine, Amsterdam University Medical Centers, Cancer Center Amsterdam, Amsterdam, The Netherlands
| | - Matthijs C. F. Cysouw
- Department of Radiology & Nuclear Medicine, Amsterdam University Medical Centers, Cancer Center Amsterdam, Amsterdam, The Netherlands
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11
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Chan TH, Haworth A, Wang A, Osanlouy M, Williams S, Mitchell C, Hofman MS, Hicks RJ, Murphy DG, Reynolds HM. Detecting localised prostate cancer using radiomic features in PSMA PET and multiparametric MRI for biologically targeted radiation therapy. EJNMMI Res 2023; 13:34. [PMID: 37099047 PMCID: PMC10133419 DOI: 10.1186/s13550-023-00984-5] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/07/2023] [Accepted: 04/17/2023] [Indexed: 04/27/2023] Open
Abstract
BACKGROUND Prostate-Specific Membrane Antigen (PSMA) PET/CT and multiparametric MRI (mpMRI) are well-established modalities for identifying intra-prostatic lesions (IPLs) in localised prostate cancer. This study aimed to investigate the use of PSMA PET/CT and mpMRI for biologically targeted radiation therapy treatment planning by: (1) analysing the relationship between imaging parameters at a voxel-wise level and (2) assessing the performance of radiomic-based machine learning models to predict tumour location and grade. METHODS PSMA PET/CT and mpMRI data from 19 prostate cancer patients were co-registered with whole-mount histopathology using an established registration framework. Apparent Diffusion Coefficient (ADC) maps were computed from DWI and semi-quantitative and quantitative parameters from DCE MRI. Voxel-wise correlation analysis was conducted between mpMRI parameters and PET Standardised Uptake Value (SUV) for all tumour voxels. Classification models were built using radiomic and clinical features to predict IPLs at a voxel level and then classified further into high-grade or low-grade voxels. RESULTS Perfusion parameters from DCE MRI were more highly correlated with PET SUV than ADC or T2w. IPLs were best detected with a Random Forest Classifier using radiomic features from PET and mpMRI rather than either modality alone (sensitivity, specificity and area under the curve of 0.842, 0.804 and 0.890, respectively). The tumour grading model had an overall accuracy ranging from 0.671 to 0.992. CONCLUSIONS Machine learning classifiers using radiomic features from PSMA PET and mpMRI show promise for predicting IPLs and differentiating between high-grade and low-grade disease, which could be used to inform biologically targeted radiation therapy planning.
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Affiliation(s)
- Tsz Him Chan
- Auckland Bioengineering Institute, The University of Auckland, Auckland, New Zealand
| | - Annette Haworth
- Institute of Medical Physics, School of Physics, The University of Sydney, Sydney, NSW, Australia
| | - Alan Wang
- Auckland Bioengineering Institute, The University of Auckland, Auckland, New Zealand
- Centre for Medical Imaging, Faculty of Medical and Health Sciences, University of Auckland, Auckland, New Zealand
- Centre for Brain Research, The University of Auckland, Auckland, New Zealand
| | - Mahyar Osanlouy
- Auckland Bioengineering Institute, The University of Auckland, Auckland, New Zealand
| | - Scott Williams
- Sir Peter MacCallum Department of Oncology, The University of Melbourne, Melbourne, VIC, Australia
- Division of Radiation Oncology, Peter MacCallum Cancer Centre, Melbourne, VIC, Australia
| | - Catherine Mitchell
- Department of Pathology, Peter MacCallum Cancer Centre, Melbourne, VIC, Australia
| | - Michael S Hofman
- Sir Peter MacCallum Department of Oncology, The University of Melbourne, Melbourne, VIC, Australia
- Cancer Imaging, Peter MacCallum Cancer Centre, Melbourne, VIC, Australia
| | - Rodney J Hicks
- Department of Medicine, St Vincent's Hospital Medical School, The University of Melbourne, Melbourne, VIC, Australia
| | - Declan G Murphy
- Division of Cancer Surgery, Peter MacCallum Cancer Centre, Melbourne, VIC, Australia
| | - Hayley M Reynolds
- Auckland Bioengineering Institute, The University of Auckland, Auckland, New Zealand.
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12
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Stanzione A, Ponsiglione A, Alessandrino F, Brembilla G, Imbriaco M. Beyond diagnosis: is there a role for radiomics in prostate cancer management? Eur Radiol Exp 2023; 7:13. [PMID: 36907973 PMCID: PMC10008761 DOI: 10.1186/s41747-023-00321-4] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/07/2022] [Accepted: 01/05/2023] [Indexed: 03/13/2023] Open
Abstract
The role of imaging in pretreatment staging and management of prostate cancer (PCa) is constantly evolving. In the last decade, there has been an ever-growing interest in radiomics as an image analysis approach able to extract objective quantitative features that are missed by human eye. However, most of PCa radiomics studies have been focused on cancer detection and characterisation. With this narrative review we aimed to provide a synopsis of the recently proposed potential applications of radiomics for PCa with a management-based approach, focusing on primary treatments with curative intent and active surveillance as well as highlighting on recurrent disease after primary treatment. Current evidence is encouraging, with radiomics and artificial intelligence appearing as feasible tools to aid physicians in planning PCa management. However, the lack of external independent datasets for validation and prospectively designed studies casts a shadow on the reliability and generalisability of radiomics models, delaying their translation into clinical practice.Key points• Artificial intelligence solutions have been proposed to streamline prostate cancer radiotherapy planning.• Radiomics models could improve risk assessment for radical prostatectomy patient selection.• Delta-radiomics appears promising for the management of patients under active surveillance.• Radiomics might outperform current nomograms for prostate cancer recurrence risk assessment.• Reproducibility of results, methodological and ethical issues must still be faced before clinical implementation.
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Affiliation(s)
- Arnaldo Stanzione
- Department of Advanced Biomedical Sciences, University of Naples Federico II, Naples, Italy
| | - Andrea Ponsiglione
- Department of Advanced Biomedical Sciences, University of Naples Federico II, Naples, Italy.
| | | | - Giorgio Brembilla
- Department of Radiology, IRCCS San Raffaele Scientific Institute, Vita-Salute San Raffaele University, Milan, Italy
| | - Massimo Imbriaco
- Department of Advanced Biomedical Sciences, University of Naples Federico II, Naples, Italy
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13
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Tenbergen CJA, Ruhm L, Ypma S, Heerschap A, Henning A, Scheenen TWJ. Improving the Effective Spatial Resolution in 1H-MRSI of the Prostate with Three-Dimensional Overdiscretized Reconstructions. Life (Basel) 2023; 13:life13020282. [PMID: 36836640 PMCID: PMC9967259 DOI: 10.3390/life13020282] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/12/2022] [Revised: 01/12/2023] [Accepted: 01/14/2023] [Indexed: 01/20/2023] Open
Abstract
In in vivo 1H-MRSI of the prostate, small matrix sizes can cause voxel bleeding extending to regions far from a voxel, dispersing a signal of interest outside that voxel and mixing extra-prostatic residual lipid signals into the prostate. To resolve this problem, we developed a three-dimensional overdiscretized reconstruction method. Without increasing the acquisition time from current 3D MRSI acquisition methods, this method is aimed to improve the localization of metabolite signals in the prostate without compromising on SNR. The proposed method consists of a 3D spatial overdiscretization of the MRSI grid, followed by noise decorrelation with small random spectral shifts and weighted spatial averaging to reach a final target spatial resolution. We successfully applied the three-dimensional overdiscretized reconstruction method to 3D prostate 1H-MRSI data at 3T. Both in phantom and in vivo, the method proved to be superior to conventional weighted sampling with Hamming filtering of k-space. Compared with the latter, the overdiscretized reconstructed data with smaller voxel size showed up to 10% less voxel bleed while maintaining higher SNR by a factor of 1.87 and 1.45 in phantom measurements. For in vivo measurements, within the same acquisition time and without loss of SNR compared with weighted k-space sampling and Hamming filtering, we achieved increased spatial resolution and improved localization in metabolite maps.
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Affiliation(s)
- Carlijn J. A. Tenbergen
- Department of Medical Imaging, Radboud University Medical Center, 6525 GA Nijmegen, The Netherlands
- Correspondence:
| | - Loreen Ruhm
- High-Field MR Center, Max Planck Institute for Biological Cybernetics, 72076 Tübingen, Germany
| | - Sjoerd Ypma
- Department of Medical Imaging, Radboud University Medical Center, 6525 GA Nijmegen, The Netherlands
| | - Arend Heerschap
- Department of Medical Imaging, Radboud University Medical Center, 6525 GA Nijmegen, The Netherlands
| | - Anke Henning
- High-Field MR Center, Max Planck Institute for Biological Cybernetics, 72076 Tübingen, Germany
- Advanced Imaging Research Center, University of Texas Southwestern Medical Center, Dallas, TX 75390, USA
| | - Tom W. J. Scheenen
- Department of Medical Imaging, Radboud University Medical Center, 6525 GA Nijmegen, The Netherlands
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14
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Ching JCF, Lam S, Lam CCH, Lui AOY, Kwong JCK, Lo AYH, Chan JWH, Cai J, Leung WS, Lee SWY. Integrating CT-based radiomic model with clinical features improves long-term prognostication in high-risk prostate cancer. Front Oncol 2023; 13:1060687. [PMID: 37205204 PMCID: PMC10186349 DOI: 10.3389/fonc.2023.1060687] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/03/2022] [Accepted: 04/10/2023] [Indexed: 05/21/2023] Open
Abstract
Objective High-risk prostate cancer (PCa) is often treated by prostate-only radiotherapy (PORT) owing to its favourable toxicity profile compared to whole-pelvic radiotherapy. Unfortunately, more than 50% patients still developed disease progression following PORT. Conventional clinical factors may be unable to identify at-risk subgroups in the era of precision medicine. In this study, we aimed to investigate the prognostic value of pre-treatment planning computed tomography (pCT)-based radiomic features and clinical attributes to predict 5-year progression-free survival (PFS) in high-risk PCa patients following PORT. Materials and methods A total of 176 biopsy-confirmed PCa patients who were treated at the Hong Kong Princess Margaret Hospital were retrospectively screened for eligibility. Clinical data and pCT of one hundred eligible high-risk PCa patients were analysed. Radiomic features were extracted from the gross-tumour-volume (GTV) with and without applying Laplacian-of-Gaussian (LoG) filter. The entire patient cohort was temporally stratified into a training and an independent validation cohort in a ratio of 3:1. Radiomics (R), clinical (C) and radiomic-clinical (RC) combined models were developed by Ridge regression through 5-fold cross-validation with 100 iterations on the training cohort. A model score was calculated for each model based on the included features. Model classification performance on 5-year PFS was evaluated in the independent validation cohort by average area-under-curve (AUC) of receiver-operating-characteristics (ROC) curve and precision-recall curve (PRC). Delong's test was used for model comparison. Results The RC combined model which contains 6 predictive features (tumour flatness, root-mean-square on fine LoG-filtered image, prostate-specific antigen serum concentration, Gleason score, Roach score and GTV volume) was the best-performing model (AUC = 0.797, 95%CI = 0.768-0.826), which significantly outperformed the R-model (AUC = 0.795, 95%CI = 0.774-0.816) and C-model (AUC = 0.625, 95%CI = 0.585-0.665) in the independent validation cohort. Besides, only the RC model score significantly classified patients in both cohorts into progression and progression-free groups regarding their 5-year PFS (p< 0.05). Conclusion Combining pCT-based radiomic and clinical attributes provided superior prognostication value regarding 5-year PFS in high-risk PCa patients following PORT. A large multi-centre study will potentially aid clinicians in implementing personalised treatment for this vulnerable subgroup in the future.
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Affiliation(s)
- Jerry C. F. Ching
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong, Hong Kong SAR, China
| | - Saikit Lam
- Department of Biomedical Engineering, The Hong Kong Polytechnic University, Hong Kong, Hong Kong SAR, China
- Research Institute for Smart Aging, The Hong Kong Polytechnic University, Hong Kong, Hong Kong SAR, China
| | - Cody C. H. Lam
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong, Hong Kong SAR, China
| | - Angie O. Y. Lui
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong, Hong Kong SAR, China
| | - Joanne C. K. Kwong
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong, Hong Kong SAR, China
| | - Anson Y. H. Lo
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong, Hong Kong SAR, China
| | - Jason W. H. Chan
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong, Hong Kong SAR, China
| | - Jing Cai
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong, Hong Kong SAR, China
| | - W. S. Leung
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong, Hong Kong SAR, China
- *Correspondence: Shara W. Y. Lee, ; W. S. Leung,
| | - Shara W. Y. Lee
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong, Hong Kong SAR, China
- *Correspondence: Shara W. Y. Lee, ; W. S. Leung,
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15
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Ghezzo S, Mongardi S, Bezzi C, Samanes Gajate AM, Preza E, Gotuzzo I, Baldassi F, Jonghi-Lavarini L, Neri I, Russo T, Brembilla G, De Cobelli F, Scifo P, Mapelli P, Picchio M. External validation of a convolutional neural network for the automatic segmentation of intraprostatic tumor lesions on 68Ga-PSMA PET images. Front Med (Lausanne) 2023; 10:1133269. [PMID: 36910493 PMCID: PMC9995820 DOI: 10.3389/fmed.2023.1133269] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/28/2022] [Accepted: 02/07/2023] [Indexed: 02/25/2023] Open
Abstract
Introduction State of the art artificial intelligence (AI) models have the potential to become a "one-stop shop" to improve diagnosis and prognosis in several oncological settings. The external validation of AI models on independent cohorts is essential to evaluate their generalization ability, hence their potential utility in clinical practice. In this study we tested on a large, separate cohort a recently proposed state-of-the-art convolutional neural network for the automatic segmentation of intraprostatic cancer lesions on PSMA PET images. Methods Eighty-five biopsy proven prostate cancer patients who underwent 68Ga PSMA PET for staging purposes were enrolled in this study. Images were acquired with either fully hybrid PET/MRI (N = 46) or PET/CT (N = 39); all participants showed at least one intraprostatic pathological finding on PET images that was independently segmented by two Nuclear Medicine physicians. The trained model was available at https://gitlab.com/dejankostyszyn/prostate-gtv-segmentation and data processing has been done in agreement with the reference work. Results When compared to the manual contouring, the AI model yielded a median dice score = 0.74, therefore showing a moderately good performance. Results were robust to the modality used to acquire images (PET/CT or PET/MRI) and to the ground truth labels (no significant difference between the model's performance when compared to reader 1 or reader 2 manual contouring). Discussion In conclusion, this AI model could be used to automatically segment intraprostatic cancer lesions for research purposes, as instance to define the volume of interest for radiomics or deep learning analysis. However, more robust performance is needed for the generation of AI-based decision support technologies to be proposed in clinical practice.
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Affiliation(s)
- Samuele Ghezzo
- Department of Medicine and Surgery, Vita-Salute San Raffaele University, Milan, Italy.,Department of Nuclear Medicine, IRCCS San Raffaele Scientific Institute, Milan, Italy
| | - Sofia Mongardi
- Department of Medicine and Surgery, Vita-Salute San Raffaele University, Milan, Italy
| | - Carolina Bezzi
- Department of Medicine and Surgery, Vita-Salute San Raffaele University, Milan, Italy.,Department of Nuclear Medicine, IRCCS San Raffaele Scientific Institute, Milan, Italy
| | | | - Erik Preza
- Department of Nuclear Medicine, IRCCS San Raffaele Scientific Institute, Milan, Italy
| | - Irene Gotuzzo
- School of Medicine and Surgery, University of Milano-Bicocca, Monza, Italy
| | - Francesco Baldassi
- School of Medicine and Surgery, University of Milano-Bicocca, Monza, Italy
| | | | - Ilaria Neri
- Department of Medicine and Surgery, Vita-Salute San Raffaele University, Milan, Italy.,Department of Nuclear Medicine, IRCCS San Raffaele Scientific Institute, Milan, Italy
| | - Tommaso Russo
- Department of Medicine and Surgery, Vita-Salute San Raffaele University, Milan, Italy.,Department of Radiology, IRCCS San Raffaele Scientific Institute, Milan, Italy
| | - Giorgio Brembilla
- Department of Medicine and Surgery, Vita-Salute San Raffaele University, Milan, Italy.,Department of Radiology, IRCCS San Raffaele Scientific Institute, Milan, Italy
| | - Francesco De Cobelli
- Department of Medicine and Surgery, Vita-Salute San Raffaele University, Milan, Italy.,Department of Radiology, IRCCS San Raffaele Scientific Institute, Milan, Italy
| | - Paola Scifo
- Department of Nuclear Medicine, IRCCS San Raffaele Scientific Institute, Milan, Italy
| | - Paola Mapelli
- Department of Medicine and Surgery, Vita-Salute San Raffaele University, Milan, Italy.,Department of Nuclear Medicine, IRCCS San Raffaele Scientific Institute, Milan, Italy
| | - Maria Picchio
- Department of Medicine and Surgery, Vita-Salute San Raffaele University, Milan, Italy.,Department of Nuclear Medicine, IRCCS San Raffaele Scientific Institute, Milan, Italy
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16
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Beyond Multiparametric MRI and towards Radiomics to Detect Prostate Cancer: A Machine Learning Model to Predict Clinically Significant Lesions. Cancers (Basel) 2022; 14:cancers14246156. [PMID: 36551642 PMCID: PMC9776977 DOI: 10.3390/cancers14246156] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/08/2022] [Revised: 12/08/2022] [Accepted: 12/12/2022] [Indexed: 12/15/2022] Open
Abstract
The risk of misclassifying clinically significant prostate cancer (csPCa) by multiparametric magnetic resonance imaging is consistent, also using the updated PIRADS score and although different definitions of csPCa, patients with Gleason Grade group (GG) ≥ 3 have a significantly worse prognosis. This study aims to develop a machine learning model predicting csPCa (i.e., any GG ≥ 3 lesion at target biopsy) by mpMRI radiomic features and analyzing similarities between GG groups. One hundred and two patients with 117 PIRADS ≥ 3 lesions at mpMRI underwent target+systematic biopsy, providing histologic diagnosis of PCa, 61 GG < 3 and 56 GG ≥ 3. Features were generated locally from an apparent diffusion coefficient and selected, using the LASSO method and Wilcoxon rank-sum test (p < 0.001), to achieve only four features. After data augmentation, the features were exploited to train a support vector machine classifier, subsequently validated on a test set. To assess the results, Kruskal−Wallis and Wilcoxon rank-sum tests (p < 0.001) and receiver operating characteristic (ROC)-related metrics were used. GG1 and GG2 were equivalent (p = 0.26), whilst clear separations between either GG[1,2] and GG ≥ 3 exist (p < 10−6). On the test set, the area under the curve = 0.88 (95% CI, 0.68−0.94), with positive and negative predictive values being 84%. The features retain a histological interpretation. Our model hints at GG2 being much more similar to GG1 than GG ≥ 3.
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Liu X, Elbanan MG, Luna A, Haider MA, Smith AD, Sabottke CF, Spieler BM, Turkbey B, Fuentes D, Moawad A, Kamel S, Horvat N, Elsayes KM. Radiomics in Abdominopelvic Solid-Organ Oncologic Imaging: Current Status. AJR Am J Roentgenol 2022; 219:985-995. [PMID: 35766531 PMCID: PMC10616929 DOI: 10.2214/ajr.22.27695] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
Abstract
Radiomics is the process of extraction of high-throughput quantitative imaging features from medical images. These features represent noninvasive quantitative biomarkers that go beyond the traditional imaging features visible to the human eye. This article first reviews the steps of the radiomics pipeline, including image acquisition, ROI selection and image segmentation, image preprocessing, feature extraction, feature selection, and model development and application. Current evidence for the application of radiomics in abdominopelvic solid-organ cancers is then reviewed. Applications including diagnosis, subtype determination, treatment response assessment, and outcome prediction are explored within the context of hepatobiliary and pancreatic cancer, renal cell carcinoma, prostate cancer, gynecologic cancer, and adrenal masses. This literature review focuses on the strongest available evidence, including systematic reviews, meta-analyses, and large multicenter studies. Limitations of the available literature are highlighted, including marked heterogeneity in radiomics methodology, frequent use of small sample sizes with high risk of overfitting, and lack of prospective design, external validation, and standardized radiomics workflow. Thus, although studies have laid a foundation that supports continued investigation into radiomics models, stronger evidence is needed before clinical adoption.
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Affiliation(s)
- Xiaoyang Liu
- Joint Department of Medical Imaging, Division of Abdominal Imaging, University Health Network, University of Toronto, ON, Canada
| | - Mohamed G Elbanan
- Department of Radiology, Yale New Haven Health, Bridgeport Hospital, Bridgeport, CT
| | | | - Masoom A Haider
- Lunenfeld-Tanenbaum Research Institute, Sinai Health System, Toronto, ON, Canada
- Joint Department of Medical Imaging, University Health Network, Sinai Health System and University of Toronto, Toronto, ON, Canada
| | - Andrew D Smith
- Department of Radiology, University of Alabama at Birmingham, Birmingham, AL
| | - Carl F Sabottke
- Department of Medical Imaging, University of Arizona College of Medicine, Tucson, AZ
| | - Bradley M Spieler
- Department of Radiology, University Medical Center, Louisiana State University Health Sciences Center, New Orleans, LA
| | - Baris Turkbey
- Molecular Imaging Program, National Cancer Institute, NIH, Bethesda, MD
| | - David Fuentes
- Department of Imaging Physics, University of Texas MD Anderson Cancer Center, Houston, TX
| | - Ahmed Moawad
- Department of Diagnostic and Interventional Radiology, Mercy Catholic Medical Center, Darby, PA
| | - Serageldin Kamel
- Department of Lymphoma, University of Texas MD Anderson Cancer Center, Houston, TX
| | - Natally Horvat
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY
| | - Khaled M Elsayes
- Department of Abdominal Imaging, University of Texas MD Anderson Cancer Center, 1400 Pressler St, Houston, TX 77030
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Michalet M, Riou O, Azria D, Decoene C, Crop F. News in magnetic resonance imaging use for radiation oncology. Cancer Radiother 2022; 26:784-788. [PMID: 36031496 DOI: 10.1016/j.canrad.2022.06.028] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/21/2022] [Revised: 06/28/2022] [Accepted: 06/30/2022] [Indexed: 11/29/2022]
Abstract
The purpose of this article is to give a summary of the progress of magnetic resonance imaging (MRI) in radiotherapy. MRI is an important imaging modality for treatment planning in radiotherapy. However, the registration step with the simulation scanner can be a source of errors, motivating the implementation of all-MRI simulation methods and new accelerators coupled with on-board MRI. First, practical MRI imaging for radiotherapy is detailed, but also the importance of a coherent imaging workflow incorporating all imaging modalities. Second, future evolutions and research domains such as quantitative imaging biomarkers, MRI-only pseudo computed tomography and radiomics are discussed. Finally, the application of MRI during radiotherapy treatment is reviewed: the use of MR-linear accelerators. MRI is increasingly integrated into radiotherapy. Advances in diagnostic imaging can thus benefit radiotherapy, but specific radiotherapy constraints lead to additional challenges and require close collaboration between radiologists, radiation oncologists, technologists and physicists. The integration of quantitative imaging biomarkers in the radiotherapy process will result in mutual benefit for diagnostic imaging and radiotherapy. MRI-guided radiotherapy has already been used for several years in clinical routine. Abdominopelvic neoplasias (pancreas, liver, prostate) are the preferred locations for treatment because of their favourable contrast in MRI, their movement during irradiation and their proximity to organs at risk of radiation exposure, making the tracking and daily adaptation of the plan essential. MRI has emerged as an increasingly necessary imaging modality for radiotherapy planning. Inclusion of patients in clinical trials evaluating new MRI-guided radiotherapy techniques and associated quantitative imaging biomarkers will be necessary to assess the benefits.
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Affiliation(s)
- M Michalet
- Institut du cancer de Montpellier, Fédération universitaire d'oncologie-radiothérapie d'Occitanie Méditerranée (Forom), Inserm U1194 IRCM, 208, avenue des Apothicaires, 34298 Montpellier, France.
| | - O Riou
- Institut du cancer de Montpellier, Fédération universitaire d'oncologie-radiothérapie d'Occitanie Méditerranée (Forom), Inserm U1194 IRCM, 208, avenue des Apothicaires, 34298 Montpellier, France
| | - D Azria
- Institut du cancer de Montpellier, Fédération universitaire d'oncologie-radiothérapie d'Occitanie Méditerranée (Forom), Inserm U1194 IRCM, 208, avenue des Apothicaires, 34298 Montpellier, France
| | - C Decoene
- Medical physics, centre Oscar-Lambret, 3, rue Frédéric-Combemale, 59000 Lille, France
| | - F Crop
- Medical physics, centre Oscar-Lambret, 3, rue Frédéric-Combemale, 59000 Lille, France
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